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postdoctoral Research Associate (computational biology and bioinformatics area) position is open at Duke University School of Medicine in the lab of Dr. Yi Zhang starting Mar 2026 or later. The ideal candidate
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: • Animal handling experience. • Intellectual curiosity and a growth mindset. • Biostatistics and bioinformatics expertise for scRNA-seq analysis. • Experience with molecular, cellular, biochemical, and
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and statistical analysis sections (including power analysis) for research grant proposals. •Work effectively with other members of the research team. Earned a doctorate (PhD, MD/MPH, etc.) in health
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to support your career goals and provide access to state-of-the-art facilities. Candidates with background knowledge and hands-on experience in transcriptional regulation, 3D-organoids and bioinformatics
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: Post-Doctoral Associate Position Summary: The Department of Biostatistics & Bioinformatics (B&B) at the Duke University School of Medicine engages in methodological and collaborative research, providing
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/bioinformatics, and data science. Work Performed · Work in highly collaborative inter-disciplinary environment with clinicians, econometricians, statisticians, and data scientists · Lead statistical analysis
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presentation skills for engaging diverse stakeholders. Minimum Requirements: Requires a minimum of a PhD in disciplines such as epidemiology, health demography, bioinformatics, statistics, computer science
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, Bioinformatics, Phylogenetics, or related fields and evidence of strong research productivity. The Research Associate will be responsible for: 1) conducting laboratory research, 2) data analysis, and 3
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, grant applications and publication in professional journals. • Implementation of bioinformatics approaches. • Conformance to standards of responsible conduct in research Compliance with good scholarly and
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healthcare. Qualifications Required: PhD (or equivalent) in computer science, statistics, biostatistics, electrical/biomedical engineering, or related quantitative field. Strong background in machine learning